Sharad Silwal

Analysis, Applied Mathematics, Statistics

PhD
7.23

Publications

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    Sapto Indratno · Diego Maldonado · Sharad Silwal
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    ABSTRACT: In this expository article we introduce a diagrammatic scheme to represent reverse classes of weights and some of their properties.
    Full-text · Article · Nov 2013 · Expositiones Mathematicae
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    Sapto Indratno · Diego Maldonado · Sharad Silwal
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    ABSTRACT: We introduce a novel approach towards Harnackʼs inequality in the context of spaces of homogeneous type. This approach, based on the so-called critical density property and doubling properties for weights, avoids the explicit use of covering lemmas and BMO.
    Preview · Article · Apr 2013 · Journal of Differential Equations
  • Diego Maldonado · Sharad Silwal · Haiyan Wang
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    ABSTRACT: Full-reference image quality assessment methods seek to measure visual similarity between two images (in practice, one original and the other its altered version). It has been established that traditional methods, such as Mean Square Error and Peak Signal-to-Noise Ratio poorly mimic the human visual system and much of the recent research in image quality assessment has been directed toward developing image similarity measures that are more consistent with assessments from human observers. Some extensively tested popular methods in this regard are Visual Image Fidelity (VIF), Structure Similarity Index (SSIM) and its variants Multi-scale Structure Similarity Index (MS-SSIM) and Information Content Weighted Multi-scale Structure Similarity Index (IW-SSIM). However, experiments show that these methods may produce drastically different similarity indices for different images contaminated with the same source of random noise. In this article, we propose a new full-reference image quality assessment method, namely, Wavelet-based Non-parametric Structure Similarity Index (WNPSSIM), specifically designed to detect visual similarity between images contaminated with all sorts of random noises. WNPSSIM is based on a rank test of the hypothesis of identical images conducted on the wavelet domain. Our experimental comparisons demonstrate that WNPSSIM provides similar ranking as MS-SSIM, IW-SSIM and VIF for images contaminated with different random noises in general though the methodology is very different. In addition, WNPSSIM corrects the aforementioned shortcoming of assigning sharply different similarity indices for different images contaminated with the same source of random noise.
    No preview · Article · Jan 2013 · Statistics and its interface
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    Haiyan Wang · Diego Maldonado · Sharad Silwal
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    ABSTRACT: In image processing, image similarity indices evaluate how much structural information is maintained by a processed image in relation to a reference image. Commonly used measures, such as the mean squared error (MSE) and peak signal to noise ratio (PSNR), ignore the spatial information (e.g. redundancy) contained in natural images, which can lead to an inconsistent similarity evaluation from the human visual perception. Recently, a structural similarity measure (SSIM), that quantifies image fidelity through estimation of local correlations scaled by local brightness and contrast comparisons, was introduced by Wang et al. (2004). This correlation-based SSIM outperforms MSE in the similarity assessment of natural images. However, as correlation only measures linear dependence, distortions from multiple sources or nonlinear image processing such as nonlinear filtering can cause SSIM to under- or overestimate the true structural similarity. In this article, we propose a new similarity measure that replaces the correlation and contrast comparisons of SSIM by a term obtained from a nonparametric test that has superior power to capture general dependence, including linear and nonlinear dependence in the conditional mean regression function as a special case. The new similarity measure applied to images from noise contamination, filtering, and watermarking, provides a more consistent image structural fidelity measure than commonly used measures.
    Preview · Article · Nov 2011 · Computational Statistics & Data Analysis

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